ML Engineer

Responsibilities

• Package and deploy models (ASR, translation, OCR, NER, summarization) using Triton/KServe on Kubernetes.
• Build evaluation pipelines (WER, BLEU, F1, latency, cost) and automate release gating.
• Operate streaming + batch inference via Kafka, Temporal, and backfill tooling.
• Monitor drift/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance.
• Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop feedback loops.
• About you
• 4-8+ years ML engineering/MLOps, shipping models to production.
• Strong Python, PyTorch/Transformers, and experience with Triton/KServe or similar.
• Comfortable with Kubernetes, GitOps, CI/CD, and GPU workload operations.
• Knowledge of evaluation metrics, monitoring, and annotation workflows.
• Eligible to work in Germany; export-control screening required for certain programs.

Requirements

• Temporal, Beam/Flink, or Ray Serve experience; ONNX/TensorRT optimization.
• German language (B1+) and familiarity with defense or public safety datasets.
• WhisperX, DeepStream/GStreamer, or vector search integrations.

Benefits

Additional Information

Company Orcrist builds the Orcrist Intelligence Platform (OIP), a secure, Kubernetes-native data intelligence system deployed as SaaS or self-hosted/on-prem (including air-gapped missions). We fuse data processing, ML, and intuitive UX for defense, law-enforcement, and enterprise teams. Role Productionize the NLP/audio/document models that power OIP's insight experiences. You'll own model packaging, deployment, monitoring, and evaluation-partnering with Research and product squads to deliver trustworthy enrichment worldwide.

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Company Intel

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